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Professional-Data-Engineer入門知識認定試験に合格することは難しいようですね。
Google Cloud Certified Professional-Data-Engineer入門知識 - Google Certified Professional Data Engineer Exam 無料な部分ダウンロードしてください。 ここには、私たちは君の需要に応じます。OmgzlookのGoogleのProfessional-Data-Engineer 日本語復習赤本問題集を購入したら、私たちは君のために、一年間無料で更新サービスを提供することができます。
Omgzlook のGoogleのProfessional-Data-Engineer入門知識認証証明書はあなたが自分の知識と技能を高めることに助けになれることだけでなく、さまざまな条件であなたのキャリアを助けることもできます。Omgzlook のGoogleのProfessional-Data-Engineer入門知識問題集を利用することをお勧めいたします。Omgzlook のGoogleのProfessional-Data-Engineer入門知識問題集は最も徹底的で、最も正確で、かつアップ·ツー·デートなものです。
Google Professional-Data-Engineer入門知識 - IT職員のキャリアと関連しますから。
現在IT技術会社に通勤しているあなたは、GoogleのProfessional-Data-Engineer入門知識試験認定を取得しましたか？Professional-Data-Engineer入門知識試験認定は給料の増加とジョブのプロモーションに役立ちます。短時間でProfessional-Data-Engineer入門知識試験に一発合格したいなら、我々社のGoogleのProfessional-Data-Engineer入門知識資料を参考しましょう。また、Professional-Data-Engineer入門知識問題集に疑問があると、メールで問い合わせてください。
あなたもきっとそう思うでしょう。ですから、躊躇しないではやく試験を申し込みましょう。
Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
A. 'bigquery-public-data.noaa_gsod.gsod*`
B. 'bigquery-public-data.noaa_gsod.gsod'*
C. 'bigquery-public-data.noaa_gsod.gsod'
D. bigquery-public-data.noaa_gsod.gsod*
Answer: A
QUESTION NO: 2
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C
QUESTION NO: 3
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D
QUESTION NO: 4
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query
BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Load the data every 30 minutes into a new partitioned table in BigQuery.
Answer: D
QUESTION NO: 5
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
A. A Dataproc cluster cannot have only preemptible workers.
B. Preemptible workers cannot store data.
C. Preemptible workers cannot use persistent disk.
D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
Answer: A,B
Explanation
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms
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Updated: May 27, 2022